PySparkJob

A Dataproc job for running Apache PySpark applications on YARN.

JSON representation 
{ "mainPythonFileUri": string, "args": [ string ], "pythonFileUris": [ string ], "jarFileUris": [ string ], "fileUris": [ string ], "archiveUris": [ string ], "properties": { string: string, ... }, "loggingConfig": { object (LoggingConfig) } }
Fields
mainPythonFileUri

string

Required. The HCFS URI of the main Python file to use as the driver. Must be a .py file.
args[]

string

Optional. The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.
pythonFileUris[]

string

Optional. HCFS file URIs of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip.
jarFileUris[]

string

Optional. HCFS URIs of jar files to add to the CLASSPATHs of the Python driver and tasks.
fileUris[]

string

Optional. HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.
archiveUris[]

string

Optional. HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.

Note: Spark applications must be deployed in cluster mode for correct environment propagation.
properties

map (key: string, value: string)

Optional. A mapping of property names to values, used to configure PySpark. Properties that conflict with values set by the Dataproc API might be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.

An object containing a list of "key": value pairs. Example: { "name": "wrench", "mass": "1.3kg", "count": "3" }.
loggingConfig

object (LoggingConfig)

Optional. The runtime log config for job execution.